Data Science Training

 

🔹 DATA SCIENCE SYLLABUS

MODULE 1: Introduction to Data Science

• What is Data Science?
• Data Science Lifecycle (Data Collection → Wrangling → Analysis → Modeling → Communication)
• Roles: Data Scientist, Data Analyst, Data Engineer
• Applications: Finance, Healthcare, Marketing, E-commerce, Social Media

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MODULE 2: Mathematics & Statistics for Data Science
A. Linear Algebra
• Vectors, Matrices, Tensors
• Matrix Operations
• Eigenvalues & Eigenvectors
• Applications in ML (e.g., PCA)
B. Calculus
• Derivatives and Gradients
• Partial Derivatives
• Chain Rule
• Applications in Optimization (e.g., Gradient Descent)
C. Probability and Statistics
• Descriptive Statistics: Mean, Median, Mode, Variance, Skewness
• Probability Theory: Bayes Theorem, Conditional Probability
• Distributions: Normal, Binomial, Poisson, Uniform
• Hypothesis Testing: p-value, t-test, ANOVA, Chi-Square
• Confidence Intervals and Z-scores
• Central Limit Theorem

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MODULE 3: Programming for Data Science
• Python or R (Primary Language)
• Variables, Loops, Functions
• List Comprehensions, Lambda Functions
• Error Handling, File I/O
• Object-Oriented Programming (OOP)
• Python Libraries:
  • NumPy (Arrays and Linear Algebra)
  • Pandas (DataFrames, Data Cleaning, Merging)
  • Matplotlib & Seaborn (Data Visualization)
  • Scikit-learn (ML Models)
  • Statsmodels (Statistical Analysis)

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MODULE 4: Data Wrangling & Preprocessing
• Data Collection Techniques: APIs, Web Scraping, SQL
• Handling Missing Values
• Data Cleaning: Duplicates, Typos, Outliers
• Data Transformation: Normalization, Standardization
• Feature Engineering
• Encoding Categorical Variables (One-Hot, Label Encoding)
• Date/Time Handling

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MODULE 5: Exploratory Data Analysis (EDA)
• Univariate Analysis
• Bivariate & Multivariate Analysis
• Correlation Analysis
• Boxplots, Histograms, Heatmaps
• Detecting Outliers
• Business Understanding from Data Patterns

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MODULE 6: Machine Learning Fundamentals
Supervised Learning:
• Linear Regression
• Logistic Regression
• Decision Trees and Random Forests
• K-Nearest Neighbors (KNN)
• Support Vector Machines (SVM)
• Naive Bayes Classifier
Unsupervised Learning:
• Clustering: K-means, Hierarchical, DBSCAN
• Dimensionality Reduction: PCA, t-SNE
Model Evaluation:
• Train-Test Split, Cross-Validation
• Metrics: Accuracy, Precision, Recall, F1 Score, ROC-AUC
• Confusion Matrix

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MODULE 7: Advanced Machine Learning & Deep Learning
• Ensemble Methods: Bagging, Boosting (XGBoost, LightGBM)
• Neural Networks (ANN)
• CNNs (Computer Vision)
• RNNs and LSTM (Time Series / NLP)
• Deep Learning Frameworks: TensorFlow, Keras, PyTorch

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MODULE 8: Time Series Analysis
• Components: Trend, Seasonality, Noise
• AR, MA, ARMA, ARIMA models
• Forecasting Techniques
• Exponential Smoothing
• Prophet Model by Facebook

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MODULE 9: Natural Language Processing (NLP)
• Text Preprocessing: Tokenization, Stop Words, Stemming, Lemmatization
• Bag of Words, TF-IDF
• Word Embeddings: Word2Vec, GloVe
• Sentiment Analysis
• Topic Modeling: LDA
• Transformer Models (BERT, GPT - intro)

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MODULE 10: Data Visualization and Storytelling
• Principles of Effective Data Visualization
• Dashboards (Tableau / Power BI)
• Plotly, Altair, and Streamlit for Interactive Visuals
• Communicating Data Insights to Stakeholders
• Building Infographics

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MODULE 11: Big Data & Cloud Platforms
• Introduction to Big Data: Characteristics and Use Cases
• Hadoop Ecosystem (HDFS, MapReduce)
• Apache Spark (PySpark for ML at scale)
• Cloud Platforms:
  • AWS (S3, EC2, SageMaker)
  • Google Cloud (BigQuery)
  • Azure ML

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MODULE 12: Data Engineering Essentials
• ETL/ELT Pipelines
• Relational Databases (MySQL, PostgreSQL)
• NoSQL Databases (MongoDB)
• Data Warehousing Concepts
• Airflow for Scheduling Pipelines
• CI/CD in Data Science

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MODULE 13: MLOps & Deployment
• Model Saving (Pickle, Joblib)
• Deployment Tools: Flask, FastAPI, Docker
• Model Monitoring and Retraining
• Version Control (Git)
• MLflow for Experiment Tracking
• DevOps + Data Science Integration

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MODULE 14: Ethics and Governance in Data Science
• Data Privacy (GDPR, HIPAA)
• Fairness and Bias in AI
• Explainable AI (XAI)
• Model Interpretability (LIME, SHAP)
• Responsible AI Principles

 

🔹 TOOLS & SOFTWARE
• Languages: Python, R, SQL
• Libraries: NumPy, Pandas, Scikit-learn, TensorFlow, Keras, PyTorch, NLTK, SpaCy
• Visualization: Tableau, Power BI, Matplotlib, Plotly, Seaborn
• Big Data: Hadoop, Spark, Kafka
• Cloud: AWS, GCP, Azure

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